Literatura científica selecionada sobre o tema "Union (Hospital ship)"

The article reviews the role of Makhachkala - the capital of the Daghestan ASSR - as an industrial, transport and evacuation center in the years of the Great Patriotic War (1941-1945). The authors highlights the labor excellence of the city’s residents, especially those who worked at industrial enterprises, at the sea-port, on the railway. The importance of the city workers in strengthening the country's defense is shown. During the war, a number of factories in Makhachkala started the production of ammunition and equipment for ships and transport. The paper provides the analysis of the manufactured products, notes the joint work with the evacuated enterprises. The workers of the Makhachkala Factory named after M. Gadzhiev, for example, during the war increased the output of products by 4 times; in 1945 they 7 times won the Red Banner of the State Defense Committee and 2 times - the All-Union Central Council of Trade Unions and the People's Commissariat of the USSR Navy. The work of the citizens of Makhachkala on the construction of defensive lines, the activities of evacuation hospitals located in the city are described. In the summer of 1942, the Makhachkala special defensive line was created, while the construction of defensive structures in the city itself (barricades, firing points, shelters) was underway, in which more than 20 thousand of citizens and residents of neighbouring regions took part.

The tibia is the major bone of the lower leg, commonly referred to as the shin bone. Tibia fractures can occur from many types of injuries. Our study is to evaluate patients with different types of fracture shaft tibia. 53 patients were admitted to Al Hakim, Al Sadder & Al Forat hospitals, 39 males &14 females their ages range from 12-55 years were evaluated for different types of fracture shaft tibia in regard to shape of fracture, type of fracture whether open or close, cause of fracture, severity of injury, method of treatment, the complication of treatment, & the time of union & patient return to job. We found that closed fractures were more than open fractures & that healing of closed fractures in those treated operatively was earlier than those treated by cast but complication rate was less in casted group. So we recommend using cast in treatment of stable, closed fracture shaft tibia& that operative treatment is to be used for unstable, or for open fractures with sever soft tissue injury. Aim of studyThis study is to evaluate patients wit fractures of shaft of tibia in regard to shape of fractures, type of fractures whether open or closed, cause of fractures, severity of injury whether high or low energy trauma, method of treatment, the complications of fracture & its treatment & time of union & patient return to job.

Background: Familial bullous lichen planus (FBLP) is a rare condition. The clinical features dearly have been described. Objective: We report the largest patient series of FBLP and describe its clinical characteristics and inheritance pattern. Methods: In this retrospective chart review, we analyzed nine consecutive familial pedigrees of FBLP with 36 affected individuals who presented to the Department of Dermatology at the Wuhan Union Hospital, a tertiary referral hospital in central China. Parameters analyzed include age of onset, gender predilection, lesional distribution, nail and mucosal involvement, clinical course, and inheritance pattern. Results: Thirty-six of 85 individuals in the nine families were affected (42.4%). Females were more likely to be affected than males (58.3% vs 35.7%, Ġ(χ2 = 3.99. P < 0.05). A bimodal disease onset was found, with one peak at 1–3 years and another at 13–17 years. The shin is the most commonly affected area (97%) followed by the upper limbs and the thighs. Involvement of the torso is relatively rare. Only a minority of cases involves the oral mucosa. The disease tends to follow a chronic and progressive course. The inheritance pattern is autosomal dominant with variable penetrance. Conclusion: Familial bullous lichen planus is a chronic, progressive bullous eruption of the lower and upper extremities. Compared with non familial bullous lichen planus, it has an earlier onset and wider disease distribution. It may be inherited as an autosomal dominant condition with variable penetrance.

BackgroundPost-operative heart transplantation patients often require admission to an intensive care unit (ICU). Early prediction of the ICU length of stay (ICU-LOS) of these patients is of great significance and can guide treatment while reducing the mortality rate among patients. However, conventional linear models have tended to perform worse than non-linear models.Materials and MethodsWe collected the clinical data of 365 patients from Wuhan Union Hospital who underwent heart transplantation surgery between April 2017 and August 2020. The patients were randomly divided into training data (N = 256) and test data (N = 109) groups. 84 clinical features were collected for each patient. Features were validated using the Least Absolute Shrinkage and Selection Operator (LASSO) regression’s fivefold cross-validation method. We obtained Shapley Additive explanations (SHAP) values by executing package “shap” to interpret model predictions. Four machine learning models and logistic regression algorithms were developed. The area under the receiver operating characteristic curve (AUC-ROC) was used to compare the prediction performance of different models. Finally, for the convenience of clinicians, an online web-server was established and can be freely accessed via the website https://wuhanunion.shinyapps.io/PredictICUStay/.ResultsIn this study, 365 consecutive patients undergoing heart transplantation surgery for moderate (NYHA grade 3) or severe (NYHA grade 4) heart failure were collected in Wuhan Union Hospital from 2017 to 2020. The median age of the recipient patients was 47.2 years, while the median age of the donors was 35.58 years. 330 (90.4%) of the donor patients were men, and the average surgery duration was 260.06 min. Among this cohort, 47 (12.9%) had renal complications, 25 (6.8%) had hepatic complications, 11 (3%) had undergone chest re-exploration and 19 (5.2%) had undergone extracorporeal membrane oxygenation (ECMO). The following six important clinical features were selected using LASSO regression, and according to the result of SHAP, the rank of importance was (1) the use of extracorporeal membrane oxygenation (ECMO); (2) donor age; (3) the use of an intra-aortic balloon pump (IABP); (4) length of surgery; (5) high creatinine (Cr); and (6) the use of continuous renal replacement therapy (CRRT). The eXtreme Gradient Boosting (XGBoost) algorithm presented significantly better predictive performance (AUC-ROC = 0.88) than other models [Accuracy: 0.87; sensitivity: 0.98; specificity: 0.51; positive predictive value (PPV): 0.86; negative predictive value (NPV): 0.93].ConclusionUsing the XGBoost classifier with heart transplantation patients can provide an accurate prediction of ICU-LOS, which will not only improve the accuracy of clinical decision-making but also contribute to the allocation and management of medical resources; it is also a real-world example of precision medicine in hospitals.

PurposeNo multi-center radiomics models have been built to predict delayed remission (DR) after transsphenoidal surgery (TSS) in Cushing’s disease (CD). The present study aims to build clinical and radiomics models based on data from three centers to predict DR after TSS in CD.MethodsA total of 122 CD patients from Peking Union Medical College Hospital, Xuanwu Hospital, and Fuzhou General Hospital were enrolled between January 2000 and January 2019. The T1-weighted gadolinium-enhanced MRI images and clinical data were used as inputs to build clinical and radiomics models. The regions of interest (ROI) of MRI images were automatically defined by a deep learning algorithm developed by our team. The area under the curve (AUC) of receiver operating characteristic (ROC) curves was used to evaluate the performance of the models. In total, 10 machine learning algorithms were used to construct models.ResultsThe overall DR rate is 44.3% (54/122). According to multivariate Logistic regression analysis, patients with higher BMI and lower postoperative cortisol levels are more likely to achieve a higher rate of delayed remission. Among the 10 models, XGBoost achieved the best performance among all models in both clinical and radiomics models with AUC values of 0.767 and 0.819 respectively. The results from SHAP value and LIME algorithms revealed that postoperative cortisol level (PoC) and BMI were the most important features associated with DR.ConclusionRadiomics models can be built as an effective noninvasive method to predict DR and might be useful in assisting neurosurgeons in making therapeutic plans after TSS for CD patients. These results are preliminary and further validation in a larger patient sample is needed.

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): This work was supported by the National Institute for Research of Metabolic and Cardiovascular Diseases project (Programme EXCELES, ID Project No. LX22NPO5104) — Funded by the European Union — Next Generation EU and a personal grant from the Dutch Heart Foundation (2021T016). Background On the surface electrogram (ECG) of a normal heart, the polarity of the T-wave is concordant with the QRS complex. This phenomenon could be explained by the presumption that regions that depolarize first repolarize last. Acute changes in ventricular activation can lead to abnormal T-waves. Conduction system pacing (CSP) leads to a more physiological ventricular activation compared to conventional right ventricular pacing (RVP), but little is known about repolarization changes following CSP. Purpose To compare repolarization changes following CSP with those during RVP and normal intrinsic activation in patients requiring bradycardia treatment. Methods Ultra-high-frequency electrocardiographic (UHF-ECG) measurements were acquired at time of implantation during normal intrinsic rhythm (narrow QRS [nQRS], n=199), selective His bundle pacing (sHBP, n=50), left ventricular septal pacing (LVsp, n=87), non-selective left bundle branch pacing (nsLBBP, n=47), and RVP (n=102). Activation and repolarization times were calculated for leads V1 and V6 from the time position of the QRS negative and from the T-wave positive derivative maxima respectively. Dyssynchrony (e-DYS) was defined as the time difference between leads V6 and V1. Differences between e-DYS depolarization (e-DYS-dpt) and repolarization (e-DYS-rpt) was evaluated by Kruskal Wallis tests. Results The left panel of Figure 1 demonstrates that during both nQRS and sHBP, e-DYS-dpt is small and slightly positive, while e-DYS-rpt is negative. RVP leads to a positive and increased e-DYS-dpt and e-DYS-rpt. LVsp and nsLBBP create negative e-DYS-dpt and e-DYS-rpt values. e-DYS-dpt and e-DYS-rpt was significant different between the groups (p-values &lt;0.001). The middle and right panel of Figure 1 shows that during nQRS and sHBP, the activation-repolarization relationship is negative (~ first depolarized regions repolarize last). RVP, LVsp, and nsLBBP leads to a positive activation-repolarization relationship (~ first depolarized regions repolarize first). Conclusion sHBP preserves the physiological negative activation-repolarization relationship. RVP, LVsp, and nsLBBP reverses the activation-repolarization relationship, though smallest changes are present during LVsp. LVsp (after nQRS and sHBP) leads to the most synchronous activation and repolarization of the three.

Abstract Background Allogeneic Blood transfusion is common in hip surgery but is associated with increased morbidity. Accurate prediction of transfusion risk is necessary for minimizing blood product waste and preoperative decision-making. The study aimed to develop machine learning models for predicting perioperative blood transfusion in hip surgery and identify significant risk factors. Methods Data of patients undergoing hip surgery between January 2013 and October 2021 in the Peking Union Medical College Hospital were collected to train and test predictive models. The primary outcome was perioperative red blood cell (RBC) transfusion within 72 h of surgery. Fourteen machine learning algorithms were established to predict blood transfusion risk incorporating patient demographic characteristics, preoperative laboratory tests, and surgical information. Discrimination, calibration, and decision curve analysis were used to evaluate machine learning models. SHapley Additive exPlanations (SHAP) was performed to interpret models. Results In this study, 2431 hip surgeries were included. The Ridge Classifier performed the best with an AUC = 0.85 (95% CI, 0.81 to 0.88) and a Brier score = 0.21. Patient-related risk factors included lower preoperative hemoglobin, American Society of Anesthesiologists (ASA) Physical Status > 2, anemia, lower preoperative fibrinogen, and lower preoperative albumin. Surgery-related risk factors included longer operation time, total hip arthroplasty, and autotransfusion. Conclusions The machine learning model developed in this study achieved high predictive performance using available variables for perioperative blood transfusion in hip surgery. The predictors identified could be helpful for risk stratification, preoperative optimization, and outcomes improvement.

AbstractIn recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary to comprehensively consider the differences in equipment and technology as well as the specificity of individual patients, which heavily depend on the experience of ophthalmologists. In our study, we took advantage of machine learning to learn about the experience of ophthalmologists in decision-making and assist them in the choice of corneal refractive surgery in a new case. Our study was based on the clinical data of 7,081 patients who underwent corneal refractive surgery between 2000 and 2017 at the Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences. Due to the long data period, there were data losses and errors in this dataset. First, we cleaned the data and deleted the samples of key data loss. Then, patients were divided into three groups according to the type of surgery, after which we used SMOTE technology to eliminate imbalance between groups. Six statistical machine learning models, including NBM, RF, AdaBoost, XGBoost, BP neural network, and DBN were selected, and a ten-fold cross-validation and grid search were used to determine the optimal hyperparameters for better performance. When tested on the dataset, the multi-class RF model showed the best performance, with agreement with ophthalmologist decisions as high as 0.8775 and Macro F1 as high as 0.8019. Furthermore, the results of the feature importance analysis based on the SHAP technique were consistent with an ophthalmologist’s practical experience. Our research will assist ophthalmologists in choosing appropriate types of refractive surgery and will have beneficial clinical effects.

Chapter 5 analyzes the initial rocky years of black working-class women’s entry into the needle trades, boot and shoe, and laundry factories and their unions during the early to mid-1940s. Black working-class women exposed the fault lines of the American racial liberalism espoused by civil rights and union progressives who worked to establish “interracial good-will” in unionism and the industrial workforce. Women’s resistance on the shop floor and in the union hall, demanding respect and fairness, challenged and altered community leaders’ programs. Black working-class women were less interested in breaking the color barrier than in earning fair wages, establishing fair standards, organizing work hours around other commitments, and working and organizing in a hospitable climate. Focusing on black women’s work with the ILGWU, this chapter examines their work and union experiences in the union’s worker theater program to consider why conflicts over historical memory; black women workers’ long demands for dignity, autonomy, and respect; and social reformers’ interracial experiments produced intense battles.

Abstract At the end of July 1862, Katharine Wormeley wrote to a friend from her home in Newport, Rhode Island, lamenting the end of her twelve weeks as a nurse on the hospital ships sponsored by the United States Sanitary Commission during the three months of the Peninsular Campaign. She looked back on her experience as a time when she and others aboard the transports had “worked together under the deepest feelings, and to the extent of our powers, shoulder to shoulder, helping each other to the best of our ability, no one failing or hindering another.” Wormeley was sad, she said, “to feel that it is all over.”